PrIme Sample Attention

PrIme Sample Attention (PISA) directs the training of object detection frameworks towards prime samples. These are samples that play a key role in driving the detection performance. The authors define Hierarchical Local Rank (HLR) as a metric of importance. Specifically, they use IoU-HLR to rank positive samples and ScoreHLR to rank negative samples in each mini-batch. This ranking strategy places the positive samples with highest IoUs around each object and the negative samples with highest scores in each cluster to the top of the ranked list and directs the focus of the training process to them via a simple re-weighting scheme. The authors also devise a classification-aware regression loss to jointly optimize the classification and regression branches. Particularly, this loss would suppress those samples with large regression loss, thus reinforcing the attention to prime samples.